GEFA-YOLO:轻量级杂草检测与集团增强的融合注意力
Huicheng Li1,2, Pushi Zhao1,2, Feng Kang1,2
1College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种新的注意力机制 (GEFA),用于更准确地检测棉花杂草. GEFAY模型平衡了效率和准确性,使其适用于精密农业的边缘设备.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 棉花的杂草管理对于产量和质量至关重要.
- 现有的杂草检测模型面临着复杂的杂草形态和环境因素带来的挑战.
- 模型中的当前注意力机制在平衡性能和计算成本方面存在局限性.
研究的目的:
- 为棉花田开发一种高效准确的杂草检测模型.
- 解决农业计算机视觉模型中现有的注意力机制的局限性.
- 提出一种新的注意力机制,可以减少复杂性,同时增强特征表达.
主要方法:
- 提出了一个集成增强融合注意力机制 (GEFA),结合了集成卷积和局部空间注意力.
- 开发了集成GEFA机制的GEFAY检测模型.
- 在CottonWeedDet12,VOC和COCO数据集上评估模型.
主要成果:
- GEFAY模型在效率,准确性和复杂性之间取得了良好的平衡.
- 与传统的注意力方法相比,GEFA显示参数和计算成本的增加较小.
- 该模型显著提高了检测精度,使其适合边缘设备部署.
结论:
- GEFA机制有效地增强了功能表达,同时降低了计算负载.
- 在精准农业中,GEFAY模型为智能杂草检测提供了一个实用且可扩展的解决方案.
- 开发的系统为边缘设备提供实时杂草检测功能,支持智能农业应用程序.
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